[Quantizers] add is_compileable property to quantizers. (#11736)

add is_compileable property to quantizers.

src/diffusers/quantizers/base.py

@@ -227,3 +227,8 @@ class DiffusersQuantizer(ABC):
     @property
     @abstractmethod
     def is_trainable(self): ...
+
+    @property
+    def is_compileable(self) -> bool:
+        """Flag indicating whether the quantized model can be compiled"""
+        return False

src/diffusers/quantizers/bitsandbytes/bnb_quantizer.py

@@ -564,6 +564,10 @@ class BnB8BitDiffusersQuantizer(DiffusersQuantizer):
         # Because we're mandating `bitsandbytes` 0.43.3.
         return True
 
+    @property
+    def is_compileable(self) -> bool:
+        return True
+
     def _dequantize(self, model):
         from .utils import dequantize_and_replace
 

src/diffusers/quantizers/gguf/gguf_quantizer.py

@@ -146,6 +146,10 @@ class GGUFQuantizer(DiffusersQuantizer):
     def is_trainable(self) -> bool:
         return False
 
+    @property
+    def is_compileable(self) -> bool:
+        return True
+
     def _dequantize(self, model):
         is_model_on_cpu = model.device.type == "cpu"
         if is_model_on_cpu:

src/diffusers/quantizers/quanto/quanto_quantizer.py

@@ -175,3 +175,7 @@ class QuantoQuantizer(DiffusersQuantizer):
     @property
     def is_serializable(self):
         return True
+
+    @property
+    def is_compileable(self) -> bool:
+        return True

src/diffusers/quantizers/torchao/torchao_quantizer.py

@@ -335,3 +335,7 @@ class TorchAoHfQuantizer(DiffusersQuantizer):
     @property
     def is_trainable(self):
         return self.quantization_config.quant_type.startswith("int8")
+
+    @property
+    def is_compileable(self) -> bool:
+        return True